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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2022

23.09.2021

Complex Valued Deep Neural Networks for Nonlinear System Modeling

verfasst von: Mario Lopez-Pacheco, Wen Yu

Erschienen in: Neural Processing Letters | Ausgabe 1/2022

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Abstract

Deep learning models, such as convolutional neural networks (CNN), have been successfully applied in pattern recognition and system identification recent years. But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling. In this paper, complex valued convolution neural network (CVCNN) is presented for modeling nonlinear systems with large uncertainties. Novel training methods are proposed for CVCNN. Comparisons with other classical neural networks are made to show the advantages of the proposed methods.
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Metadaten
Titel
Complex Valued Deep Neural Networks for Nonlinear System Modeling
verfasst von
Mario Lopez-Pacheco
Wen Yu
Publikationsdatum
23.09.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10644-1

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